mc.ranks: MC based rank aggregation

In TopKLists: Inference, Aggregation and Visualization for Top-K Ranked Lists

Description

Compute aggregate ranks based on the transition matrix from the three Markov Chain algorithms.

Usage

MC.ranks(elements, trans, a, delta)

Arguments

elements	Unique elements of the union of all input lists - second element of the output list from function trans.matrix
trans	One of the three transition matrices build by function trans.matrix - 4 (5 or 6) elements of the output list from function trans.matrix
a	Tuning parameter to make sure Markov Chain with the transition matrix is ergodic; parameter value passed from MC.
delta	Convergence criterion for stationary distribution; parameter value passed from MC.

Details

Compute stationary distribution based on a Markov Chain transition matrix built with function trans.matrix.

Value

A list with 3 components:

comp1	Number of iterations to reach the stationary distribution
comp2	The stationary distribution
comp3	The rankings based on the stationary distribution

Author(s)

Shili Lin <[e-mail]>

References

Lin, S. (2010) Space oriented rank-based data integration. Statistical Applications in Genetics and Molecular Biology 9, Article 20.

See Also

MC, trans.matrix

TopKLists documentation built on May 2, 2019, 4:41 p.m.